Introduction to 10 701 Machine Learning Fall 2014 Lecture 20
If you are looking for information about 10 701 Machine Learning Fall 2014 Lecture 20, you have come to the right place. Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians
10 701 Machine Learning Fall 2014 Lecture 20 Comprehensive Overview
Graphical models: junction trees, belief propagation. Note that the first Topics: expectation maximization (EM), convergence of EM, principal component analysis (PCA) Introduction to
Topics: review of d-separation, probably approximately correct (PAC) bounds, Vapnik–Chervonenkis (VC) dimension
Summary & Highlights for 10 701 Machine Learning Fall 2014 Lecture 20
- Topics: hidden Markov models, forward-backward algorithm, Viterbi algorithm for finding the most probable state sequence, EM ...
- Description.
- Topics: principal component analysis (PCA), deep
- Topics: error bounds for infinite hypothesis spaces, Vapnik–Chervonenkis (VC) dimension, Rademacher complexity
- Topics: course logistics, high-level overview of
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